Digital Technology and Older People: Towards a Sociological Approach to Technology Adoption in Later Life
Bibliographic record
Abstract
Despite increasing social pressure to use new digital technologies, older people’s adoption of them remains below other age groups. This article contributes a sociological dimension to exploring what facilitates learning and using digital technology in later life. We focus on the understudied group of older people who are frail, living in care homes and most likely to be digitally excluded or restricted. Drawing on data from a longitudinal mixed methods study of a co-designed communication app for older people, we explore how attempts to bridge the ‘digital divide’ unfold in time. Using the concept of affordances, we show how adoption of a new communication technology is shaped by its design, learning contexts and surrounding social actors. With this work we contribute to novel sociological understandings of technology adoption that are critical for digital inequality research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".